Ali Arefi

Recently, the regulation in several countries is opening the possibility for electricity end-users to directly transact with their neighbors, in the framework of energy communities. Energy communities equipped with both distributed generation units, mainly from photovoltaics, and storage units will favor the local balance between production and consumption during the day, with the advantages for the network of improved efficiency on the one hand, and of reduced use on the other. This chapter is aimed to analyze the impact on the operation and the planning of distribution networks where the use of energy trading between neighbors will become significant. The impact on the operation is mainly associated with the optimal scheduling of the generation and storage units available inside the local/citizen/renewable energy communities. The impact is also important for the future planning of network reinforcement since the increased adoption of neighborhood energy trading mechanisms allows deferring the grid investments.

This chapter consists of introduction, the potential of wind energy in Australia, and history of wind energy in Australia. The various wind farms and their technologies used are discussed followed by the environmental and social impact of wind energy and policies, regulations, guidelines, and rules with regard to installing wind machines in Australia. The progress of wind energy research is outlined followed by future prospects. Finally a conclusion and references are provided.

In Australia, the policy of introducing high feed-in tariff previously to encourage more consumers to install rooftop solar panels worked really well, and a large number of rooftop solar panels were installed during 2009–2012. This implies that consumers can be encouraged even more to install rooftop solar panels by offering relatively high feed-in tariff, who export electricity in particular during the peak periods or at the locations where it is difficult to supply with existing electricity transmission network. An efficient seasonal time of use feed-in tariff, therefore, can encourage more consumers to install rooftop solar panels, can improve electricity load factor, and can significantly reduce the electricity supply cost to regional consumers. An efficient feed-in tariff can also ensure that customer payback period is reduced and savings in the electricity bills are increased. This efficient feed-in tariff can help to reduce the urgent need of electricity infrastructure to meet the seasonal peak demand and reliance on the inefficient generating station which may need to run to cope with the electricity peak demand.

Energy usage in general, and electricity usage in particular, are major concerns internationally due to the increased cost of providing energy supplies and the environmental impacts of electricity generation using carbon-based fuels. If a "systems" approach is taken to understanding energy issues then both supply and demand need to be considered holistically. This paper examines two research projects in the energy area with IT tools as key deliverables, one examining supply issues and the other studying demand side issues. The supply side project used hard engineering methods to build the models and software, while the demand side project used a social science approach. While the projects are distinct, there was an overlap in personnel. Comparing the knowledge extraction, model building, implementation and interface issues of these two deliverables identifies both interesting contrasts and commonalities.

In this chapter, the role of State Estimation (SE) in smart power grids is presented. The trend of SE error with respect to the increasing of the smart grids implementation investigated. The observability analysis as a prior task of SE is demonstrated and an analytical method to consider the impedance values of the branches is developed and discussed by examples. Since most principles of smart power grids are appropriate to distribution networks, the Distribution SE (DSE) considering load correlation is argued and illustrated by an example. The main features of smart grid SE, which is here named as “Smart Distributed SE” (SDSE), are discussed. Some characteristics of proposed SDES are distributed, hybrid, multi-micro grid and islanding support, Harmonic State Estimation (HSE), observability analysis and restore, error processing, and network parameter estimation. Distribution HSE (DHSE) and meter placement for SDSE are also presented.